1. Field of the Invention
The invention relates to image generation methods and related image processing systems, and more particularly, to halftone image generation method and related image processing system for converting a grayscale image into a halftone image using an improved dot diffusion method.
2. Description of the Related Art
Digital halftoning is a process to display grayscale images with a two-tone texture pattern. Halftoning is mainly used as printouts for materials such as magazines, newspapers, and books, generating a black-and-white format. Halftoning mainly takes advantage of the fact that the human visual system is not highly sensitive, so that black and white pixels of a dense uniform grid may be used to represent a desired grayscale effect. The halftoning methods include ordered dithering, dot diffusion, error diffusion, and direct binary search (DBS). Of these methods, the dot diffusion uses a class matrix and a diffused weighting and through parallel processing provides an acceptable image quality and faster processing efficiency. The dot diffusion generally used is the dot diffusion halftoning algorithm proposed by Knuth and Mese. The dot diffusion halftoning algorithm proposed by Knuth is a kind of algorithm that attempts to retain the advantages of error diffusion and simultaneously provide parallel processing. The dot diffusion has only one design parameter, namely the class matrix, which determines the order of the pixels to be halftone processed.
Based on the concept that in a class matrix the processing order will significantly affect the reconstruction image quality, the optimization proposed by Knuth aims to reduce the baron (no members of higher numerical value currently exist around the member being processed) and near-baron (only one member of higher numerical value currently exists around the member being processed) in the class matrix. Although this concept is simple and direct, the method does not consider the human visual characteristics, resulting in the generation of images that strain the human eyes. Based on an idea for improvement, Mese took into account the human visual characteristics in the optimization process of his class matrix.
However, Mese only used a single grayscale value 16 in the training set to obtain the final class matrix in his optimization process. The trained class matrix may not generate the best results when set in the natural images containing other grayscale values. Moreover, Mese's class matrix optimization process failed to take into account the diffused weighting value and the diffused area, thus limiting the space for the growth of the trained class matrix for the reconstruction of image quality.